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<a href="#pub-methods">Public Member Functions</a> &#124;
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<p><code>#include &lt;<a class="el" href="lwr_8h_source.html">lwr.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:aabc73a50c1b096e88ae7bb0c5a1ef6b8"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#aabc73a50c1b096e88ae7bb0c5a1ef6b8">LWR</a> (int numSensors, int numMotors)</td></tr>
<tr class="separator:aabc73a50c1b096e88ae7bb0c5a1ef6b8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a074100005527ba3f2294a99143dacce6"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a074100005527ba3f2294a99143dacce6">~LWR</a> ()</td></tr>
<tr class="separator:a074100005527ba3f2294a99143dacce6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9e21c991561a17d64f52fea458486cf5"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a9e21c991561a17d64f52fea458486cf5">setState</a> (Matrix motors)</td></tr>
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<tr class="memitem:abcfe8363f1707fe0d9b4fb7bcb7d66fa"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#abcfe8363f1707fe0d9b4fb7bcb7d66fa">setDesiredOut</a> (Matrix sensors)</td></tr>
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<tr class="memitem:a13787a45cb90f6189fe8885b70c65465"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a13787a45cb90f6189fe8885b70c65465">predict</a> ()</td></tr>
<tr class="separator:a13787a45cb90f6189fe8885b70c65465"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a265ec39618dce7544e3602cd79a8dd98"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a265ec39618dce7544e3602cd79a8dd98">getModelMatrix</a> ()</td></tr>
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Private Member Functions</h2></td></tr>
<tr class="memitem:af9b33fa3eb774951ce659139c446f275"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#af9b33fa3eb774951ce659139c446f275">addToMemory</a> (Matrix instance, Matrix desiredOut)</td></tr>
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<tr class="memitem:aed357f7a637671ea7dd9b27eb0e06227"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#aed357f7a637671ea7dd9b27eb0e06227">findWeights</a> ()</td></tr>
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<tr class="memitem:aa70d668c1ca5f6133f8a82afdd56ad35"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#aa70d668c1ca5f6133f8a82afdd56ad35">weightData</a> ()</td></tr>
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<tr class="memitem:a642dd3045c25dadc6b42c5a28dea9843"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a642dd3045c25dadc6b42c5a28dea9843">findRegressionCoefficieents</a> ()</td></tr>
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<tr class="memitem:aac2ff932fc3f70eb73960f5e8d08a8bf"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#aac2ff932fc3f70eb73960f5e8d08a8bf">euclideanDistance</a> (Matrix vector1, Matrix vector2)</td></tr>
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Private Attributes</h2></td></tr>
<tr class="memitem:a0ca0d792f6a4dd569fa1ef9c5444e17f"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a0ca0d792f6a4dd569fa1ef9c5444e17f">inputNum</a></td></tr>
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<tr class="memitem:a2d6394b9ef3712d0e5fe6cedbee023b0"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a2d6394b9ef3712d0e5fe6cedbee023b0">outputNum</a></td></tr>
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<tr class="memitem:a3b27e56c511b0590541189646beca69c"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a3b27e56c511b0590541189646beca69c">weights</a></td></tr>
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<tr class="memitem:a1e3e278ef9540100c9cd14fa8e5eec64"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a1e3e278ef9540100c9cd14fa8e5eec64">regressionCoeff</a></td></tr>
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<tr class="memitem:a1f9ee94677cb24599b3ee29e17f778fa"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a1f9ee94677cb24599b3ee29e17f778fa">inputHistory</a></td></tr>
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<tr class="memitem:a5c01ae8d25bf4eb9a70241e3b262ea25"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a5c01ae8d25bf4eb9a70241e3b262ea25">outputHistory</a></td></tr>
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<tr class="memitem:a1f10b43c5c2a8193a6a9a608ba1b4b25"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a1f10b43c5c2a8193a6a9a608ba1b4b25">currentInput</a></td></tr>
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<tr class="memitem:ac00e856f9ef407f234cb3f06fe534d65"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#ac00e856f9ef407f234cb3f06fe534d65">desiredOutput</a></td></tr>
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<tr class="memitem:a8420c1052435c46cbd10c16ccf1ad485"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#a8420c1052435c46cbd10c16ccf1ad485">weightedInputs</a></td></tr>
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<tr class="memitem:afc99ad32e2d3095addf80ae231548ce9"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLWR.html#afc99ad32e2d3095addf80ae231548ce9">weightedOutputs</a></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>File: <a class="el" href="lwr_8h.html">lwr.h</a></p>
<p>Class that implements locally weighted regression as predictor. The regression coefficients are updated in batch mode based on the least squares estimator. As presented in Chapter 3.2.2 of the thesis.</p>
<dl class="section author"><dt>Author</dt><dd>: Athanasios Polydoros </dd></dl>
<dl class="section version"><dt>Version</dt><dd>: 1.0 Created on 09 July 2013, 19:28 </dd></dl>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
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          <td class="memname">LWR::LWR </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>numSensors</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>numMotors</em>&#160;</td>
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          <td></td>
          <td>)</td>
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<p>Class constructor that initialise the members of class. Has to be called in the controller's init method.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">num_Sensors</td><td>The number of robot sensors, used as network's outputs </td></tr>
    <tr><td class="paramname">num_Motors</td><td>The number of motors, used as inputs </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">LWR::~LWR </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
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  <td class="mlabels-right">
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<p>Class destructor </p>

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<h2 class="groupheader">Member Function Documentation</h2>
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          <td class="memname">void LWR::addToMemory </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>instance</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>desiredOut</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
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<p>Adds to memory an input and its corresponding output </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">instance</td><td>The input </td></tr>
    <tr><td class="paramname">desiredOut</td><td>he actual output </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">double LWR::euclideanDistance </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>vector1</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>vector2</em>&#160;</td>
        </tr>
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          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
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<p>Method used for finding the euclidian distance between two vectors </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">vector1</td><td></td></tr>
    <tr><td class="paramname">vector2</td><td></td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The Euclidean distance between the input vectors </dd></dl>

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          <td class="memname">void LWR::findRegressionCoefficieents </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
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  </td>
  <td class="mlabels-right">
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<p>Calculates the regression coefficients based on the Least Square formula </p>

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          <td class="memname">void LWR::findWeights </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
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<p>Calculates the weights of each memorized input-output according to the current input (query point) based on the eucidean distance and Gaussian Kernel </p>

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          <td class="memname">Matrix LWR::getModelMatrix </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
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<p>Calculate the jacobian matrix. In the case of regression, it is simply the matrix of regression coefficients without the bias weight.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">the</td><td>input nodes values </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>MxM Jacobian matrix </dd></dl>

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          <td>(</td>
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<p>Predicts the future sensor values based on the current motor commands (inputs).</p>
<dl class="section return"><dt>Returns</dt><dd>Matrix that contains the predicted sensory values. </dd></dl>

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          <td class="memname">void LWR::setDesiredOut </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>sensors</em>)</td><td></td>
          <td></td>
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<p>Sets the desired output. This method is called within controler's method : learn() before the method</p>
<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classLWR.html#a13787a45cb90f6189fe8885b70c65465">predict()</a></dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">sensor</td><td>the robot's desired sensory values </td></tr>
  </table>
  </dd>
</dl>

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<p>Set the current inputs.</p>
<p>This method is called within controler's method : learn() before the method</p>
<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classLWR.html#a13787a45cb90f6189fe8885b70c65465">predict()</a></dd></dl>
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<p>Applies the weights at the data in the memory </p>

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<h2 class="groupheader">Member Data Documentation</h2>
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<p>Query point </p>

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<p>History of inputs after weighting </p>

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<p>History of outputs after weighting </p>

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<hr/>The documentation for this class was generated from the following files:<ul>
<li><a class="el" href="lwr_8h_source.html">lwr.h</a></li>
<li><a class="el" href="lwr_8cpp.html">lwr.cpp</a></li>
</ul>
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